Issues, Challenges and Development in Machine Learning | AIJ 2025 (12+) | Arabic скачать в хорошем качестве

Issues, Challenges and Development in Machine Learning | AIJ 2025 (12+) | Arabic

Speaker — Dianhui Wang, Qingdao University of Science and Technology During the session, issues, challenges, and developments in the field of machine learning, particularly within the context of industrial applications, were discussed. The speaker, Professor Dianhui Wang from Qingdao University of Science and Technology, presented his research and experience in this area. He noted that despite the popularity of deep learning, simpler methods often suffice for real-world industrial applications. Key discussion topics included challenges related to the reliability and efficiency of machine learning models. Professor Wang emphasized that many contemporary approaches, such as multilayer perceptrons with backpropagation, do not always deliver stable and accurate results. He also highlighted the importance of weight initialization in models and proposed using stochastic configuration networks as a more effective solution. During the discussion, various solutions for improving machine learning in industry were proposed. Professor Wang presented his development—the stochastic configuration network—which he stated outperforms multilayer perceptrons in both speed and accuracy. He also stressed the importance of using lightweight models for next-generation automation, which can rapidly adapt to real-time changes. Opportunities and prospects for applying machine learning in industry were discussed. The professor noted that lightweight models could be particularly useful in scenarios with limited data and resources. He also expressed confidence that stochastic configurations and other innovative approaches could form the foundation for next-generation automation systems. In conclusion, it was stated that machine learning holds significant potential for enhancing manufacturing processes. However, realizing this potential requires continued research and development in reliable and efficient models. Professor Dianhui Wang expressed hope for further collaboration and knowledge exchange in this field. #aijourney #aijourney2025 #artificialintelligence #aiconference #artificialintelligenceconference #ai